logicreplaces intuition,promotingacognitiveapproach for developingtactics.CPS places anemphasis on predicting when and where fire behavior may change, and it provides a nomenclature forexplaining these changes.

CPS

developsa strong case for acting ona

fire’s potential rather thansimplyreacting to changes initsbehavior.

In retrospect,manywildland firefighter deathscould have been prevented thatwere,

unfortunately,a result of reacting too late.

Fewer burn-over accidents would happen if

people couldexplain what the potential of the fire is in their situation. TheCampbell Prediction Systemprovides thelogic and language to do so.

This project will codify fire behavior observations as GIS data elements and analyze them using datamining and machine learning techniques to determine the potential fire behaviors. The goal is tographically render the potential fire behaviors relative to time and space on a 3D terrain viewer and togenerate maps that highlight both dangerous and opportunistic fire behaviors situations.

Business Objectives and Success Criteria

Business Opportunity

The current state-of-the-art fire modeling software does little to assist in tactical suppression efforts.Computer-aided wildland fire predictions can be improved. Fireground operations can benefit fromstate-of-the-art GIS enabled application. The most prominent fire modeling applications in use include

the following (overviews from FireModels.org,seehttp://www.firemodels.org

for completedescriptions):

BehavePlus–

Fire Modeling System

The BehavePlus fire modeling system is a PC-based program that is a collection of models that describefire behavior, fire effects, and the fire environment. It is a flexible systemthat produces tables, graphs,and simple diagrams and can be used for a multitude of fire management applications. BehavePlus is thesuccessor to the BEHAVE fire behavior prediction and fuel modeling system (Andrews 1986, Andrewsand Chase 1989, Burgan and

Rothermel 1984, Andrews and Bradshaw 1990). It is called the BehavePlusfire modeling system to reflect its expanded scope. Development continues with the addition of firemodeling capabilities and features to facilitate application.



Can be considered a 'point' system.



Each calculation is for a set of uniform conditions.



Rarely is a single calculation done.



The user looks at the effect of a range of values on the results.

Project Charter forWildfire Management Tool

Page2



Input is entered by the user. GIS data are not used.



Results are in the form oftables, graphs, and simple diagrams.

FARSITE

–

Fire Area Simulator

FARSITE is a fire behavior and growth simulator for use on Windows computers. It is used by FireBehavior Analysts from the USDA FS, USDI NPS, USDI BLM, and USDI BIA, and is taught in the S493course. FARSITE is designed for use by trained, professional wildland fire planners and managers familiarwith fuels, weather, topography, wildfire situations, and the associated concepts and terminology.



Automatically computes wildfire growth and behavior for long time periods underheterogeneous conditions of terrain, fuels, and weather.

FlamMap is not a replacement for FARSITE or a complete fire growth simulation model. There is notemporal component in FlamMap. It uses spatial information on topography and fuels to calculate firebehavior characteristics at one instant.

Users may need the support of ageographic information system (GIS) analyst to use FlamMapbecause it requires spatial coincident landscape raster information to run.

FSPro–

Fire Spread Probability



Probability of fire spread from a known perimeter or point.

Project Charter forWildfire Management Tool

Page3



Not a fire perimeter like FARSITE.



Not a projection of fire size.



Results are based on thousands of FARSITE simulations for simulated weather sequences.



FSPro modeling requires computing power beyond that available on a personal computer.

Business Objectives

BO-1:

Provide an effective training tool forthe Campbell Prediction System

method.

Thistool should be usable by both the student and the teacher.



Scale: The number of installations.



Meter: Internet download logs; software distribution channels.

BO-2:

Providea system, to be used on active fires, that reduces loss of life and improvesthe odds of suppression efforts through the application of tactics derived from theCampbell Prediction System.



Scale: The number of usages on fires



Meter: Number of IAPs mapswith identifying signature or watermark;number of post-incident uploads to central repository.

BO-3:

Promote the use of theCampbell Prediction Systemon the fireground.



Scale: Use of CPS language and terminology



Meter: Terms, verbiage and symbols used in incident action plans.

BO-4:

Create the opportunity torewrite

or update the Campbell Prediction Systembook

with this software as significant addition to the current text.

BO-5:

Create the opportunity to provide software support services.

Success Criteria

SC-1:

Adoptionof CPSby local, state and federal agencies.

SC-2:

Adoptionof CPSin Europe and Australia.

SC-3:

Requests for software training, documentation and support.

Project Charter forWildfire Management Tool

Page4

Stakeholders

Stakeholder

Major Benefits

Attitudes

WinConditions

Constraints

Bruce Schubert

Project Sponsor

Continuing development ofsoftware architecture andprogramming

skills;explore new technologies.

Affinity forand historywith fireservice;

High number of

downloads;internet metrics

Time

DougCampbell

CPSDomainExpert

Increased visibility andacceptance of CPStraining

and methods

Strongsupporter

Adoption of CPStraining by Federal

and Stateagencies

Retired

User Class #1

User Class #n

etc.

Vision

For

wildland firefighters and incident commandpersonnel

engaged in thesuppression of wildland fireswho

need tactical decision support tools

toensure the safety of firefighters and the effective use of firefighting resources,the

Wildfire Management Toolis adecision support system

and visualizationtool

that

predicts the potential fire behavior on the fireground

based on theCPS method;

unlike

BehavePlus andFARSITE

et al

this

product

uses on-scenefire behavior observations in addition to fire behavior calculations to identifyboth when and wheretrigger points and opportunitiesfor control exist on thefireground.

Project Scope

The goal is to develop a computer system, “… that can use

a wildland fire’s ground truths to projectthe

thresholds of control as well as the trigger points of change and the alignment runs.”

–

DougCampbell.

The system will learn to classify fire behavior based on actual fire behavior observations (ground truths)collected from the fireground. The system will blend the technologies of GIS and data mining (machinelearning) to predict potential fire behavior in the unburned landscape. This information will be used inthe planning of safety and suppression efforts on the active fire.

This system will allow the user to visualize the fireground and recognize a fire’s potential via graphicaloutput including:



Fire progression maps–

the fire’s history



Actual

fire behavior observations–

these are the ground truths

Project Charter forWildfire Management Tool

Page5



Potential

fire behavior in unburned areas–

this is what the system is to discover



Projectedtrigger points

–

the points at which fire behavior has good potential to change



Tracks of thealignment of forces–

these lines symbolize where the fire has high potential tomake head-fire runs

To accomplish the above, the system will need several inputs. First and foremost are the fire behaviorobservations. These observations are both spatial and temporal. They describe when and where theobserved fire behavior occurred. Each observation includes flame length, flame type (head, flanking, orheel), weather conditions and plume effects. Conditions defined by the landscape will be extracted byusing GIS technologies.

For inputs, the system will consume standard GIS raster data layers such as elevation, slope, aspect, andfuels. Raster layers may also include fuel temperatures in the form of georeferenced aerial IR imagery.Additional GIS layers may include raster datasets describing fuel treatments and suppression efforts. Thefire perimeter and the fire behavior observations will be represented in vector data layers.

Global data inputs will include wind, weather, and fire danger information. This data may be temporal innature, varying from day to day or from time of day.

The intersection of a fire behavior observation with the other spatial and temporal input layers producesa list of attributes which become an example, orinstance,

of fire behavior to the machine learningscheme. All of the instances collected over the life of a fire become the training and test data used in thelearning scheme. From these instances, the system will learn a way to classify potential fire behavior inthe unburned landscape.

The system will retain the fire behavior observations andfire history in a persistent database. Over thelife of the system(s), the history of several fires can be accumulated and mined for information. Duringinitial attack, or when fire behavior observations are difficult to obtain, a model fire can be selectedfrom the database and used instead.

Editing features include the ability place and annotate fire behavior observations on thelandscape via drawing tools.

FE-3:

Analysis features include the ability to discover the potentialfire behavior across thelandscape by examining the past and current fire behavior characteristics and theconditions in which they occurred. The system can determine where and when thetrigger points occur and where the forces are in alignment.

FE-4:

Rendering features include the ability to display the potential fire behaviors across thelandscape; this display can be varied by time-of-day or it can set to display themaximum potential fire behaviors. Trigger points and aligned forces can be visualized.

deliverables, their target dates, and the team role orindividual who is responsible for each one. It's not necessary to include the target date for each milestoneunless they are fixed constraints. The following table illustrates some typical milestones;

change this listas appropriate for your project.>

Event or Deliverable

Target Date

Responsibility

Project charterestablished

Project plan completed

Project plan approved

Project team assembled

Project execution initiated

Projectexecution completed

Customer acceptance

Project closed out

Business Risks

<Summarize the major business risks associated with this project, such as marketplace competition,timing issues, user acceptance, implementation issues, or possible negative impacts on the business.Estimate the severity of each risk’s potential impact and identify any risk mitigation actions that could betaken. This is not the place for the project’s overall risk list.>